Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Authors: Kaidi Cao, Jiaxuan You, Jure Leskovec
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Meta Link on 6 benchmark datasets in both biochemical and vision domains. Experiments demonstrate that Meta Link can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC. |
| Researcher Affiliation | Academia | Kaidi Cao Jiaxuan You Jure Leskovec Department of Computer Science, Stanford University {kaidicao, jiaxuan, jure}@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Meta Link Training in Relational Meta Setting |
| Open Source Code | Yes | Source code is available at https://github.com/snap-stanford/Graph Gym |
| Open Datasets | Yes | Tox21 (Huang et al., 2016), Sider (Kuhn et al., 2016), Tox Cast (Richard et al., 2016), and MS-COCO (Lin et al., 2014) |
| Dataset Splits | Yes | We search over the number of layers of [2, 3, 4, 5], and report the test set performance when the best validation set performance is reached. |
| Hardware Specification | Yes | We use one NVIDIA RTX 8000 GPU for each experiment and the most time-consuming one (MS-COCO) takes less than 24 hours. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | We use Adam optimizer, with initial learning of 0.001 and cosine learning rate scheduler. The model is trained with a batch size of 128 for 50 epochs. |